Li Fei-Fei: h-index, Total Citations, and Citation Map
Li Fei-Fei's h-index is 176 (445 i10-index, 347,517+ total citations across 701+ publications) according to Google Scholar as of May 2026. Li Fei-Fei is affiliated with Professor of Computer Science, Stanford University.
Li Fei-Fei is a researcher affiliated with Professor of Computer Science, Stanford University, specializing in Artificial Intelligence, Machine Learning, Computer Vision. Their work has been cited 347,517 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Li Fei-Fei's Citation Metrics
Bibliometric impact based on 701 indexed publications.
- H-Index
- 176
- i10-Index
- 445
- Total Citations
- 347,517
- Citing Countries
- 60
As of May 2026.
Li Fei-Fei has an h-index of 176 and 347,517 total citations across 701 publications, with research cited by institutions in 60 countries.
Global Impact Map
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Top Cited Works
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ImageNet: A Large-Scale Hierarchical Image Database
200994,654
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
934 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational large-scale hierarchical image database and recognition challenge that catalyzed the development of modern computer vision and foundation models.
The researcher pioneered socially aware trajectory prediction models, establishing a foundational framework for human motion forecasting in crowded environments that has been widely adopted across computer vision and robotics.
The researcher developed a foundational framework for segmenting arbitrary structures in medical images, establishing a versatile tool that has become a standard reference in computational pathology and medical imaging analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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